Learning How to Increase the Chance of Human-robot Engagement

The increasing use of mobile robots in social contexts makes it important to provide them with the ability to behave in the most socially acceptable way possible. In this paper we investigate the problem of making a robot learn how to approach a person in order to increase the chance of a successful engagement. We propose the use of Gaussian Process Regression (GPR), combined with ideas from reinforcement learning to make sure the space is properly and continuously explored. In the proposed example scenario, this is used by the robot to predict the best decisions in relation to its position in the environment and approach distance, each one accordingly to a certain time of the day. Numerical simulations show a significant performance improvement when compared with a random technique. The robot is able to improve performance after just one day of interaction (a few dozens of trials), and achieves the maximum expected value for the proposed approach within sixty days.